Unmanned Aircraft Systems (UAS) face one common challenge when integrating with the existing manned aircraft population in the National Airspace System (NAS). To unlock the full efficiency of UAS, the UAS integrator must comply with an onboard pilot’s requirement to see-and-avoid other aircraft while operating. Commercially available Detect-and-Avoid (DAA) sensor technologies have been developed to attempt to comply with this requirement. UAS integrators must use these sensors to meet or exceed the performance of a human pilot. This thesis covers research done to integrate an array of commercially made DAA sensors with a large Group 3 UAS both in hardware and software that was later flight tested and evaluated for usability. A fast-time simulation is presented using the principles of the National Aeronautics and Space Administration's (NASA) Detect-and-AvoID Alerting Logic for Unmanned Systems (DAIDALUS). Last, open-source tools are presented to assist future integrators in validating their DAA solutions.

Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly with GPS localization and advanced mission planning software. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy in GPS-denied environments. Developments in lidar technology and Simultaneous Localization and Mapping (SLAM) algorithms are enabling UAVs to function safely underground where they can be used to map workings and digitally reconstruct them into 3D point clouds for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drone-based lidar and photogrammetry were used to capture 3D point clouds of underground mines, and the associated applications of mine digitization, such as geotechnical analysis and pillar strength analysis. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites. / Doctor of Philosophy / Drones have become an integral part of the digital transformation currently sweeping the mining industry; particularly in surface operations, where they allow operators to model the terrain quickly and effortlessly. Recently, the usage of drones has expanded to underground mines, with advancements in drone autonomy. New developments are enabling UAVs to function safely underground where they can be used to digitally reconstruct workings for a wide variety of applications. Underground mines can be expansive with inaccessible and dangerous areas preventing safe access for traditional inspections, mapping and monitoring. In addition, abandoned mines and historic mines being reopened may lack reliable maps of sufficient detail. The underground mine environment presents a multitude of unique challenges that must be addressed for reliable drone flights. This work covers the development of drones for GPS-denied underground mines, in addition to several case studies where drones were used to create 3D models of mines, and the associated applications of mine digitization. This research also features an applied use case of custom drones built to detect methane leaks at natural gas production and distribution sites.